International Journal of Computer Applications (0975 – 8887) Volume 86 – No 11, January 2014 10 Hybrid Algorithm for Medical Image Sequences using Super-Spatial Structure Prediction with LZ8 M.Ferni Ukrit Research Scholar Department of CSE Sathyabama University Chennai G.R.Suresh Professor Department of ECE Easwari Engineering College Chennai ABSTRACT The necessity in medical image compression continuously grows during the last decade. In advanced medical life large number of medical images is processed in hospitals and medical centers around the world. These images are in the form of sequences which are much correlated and are of great importance. Hence lossless image compression is needed to reproduce the original quality of the image without any loss of information. To exploit the correlation a new algorithm is proposed in this paper. The proposed compression method combines Super-Spatial Structure Prediction with motion estimation and motion compensation to achieve higher compression ratio. This is applied with a simple block- matching process Binary Tree Search. Results are compared in terms of Compression Ratio and Peak Signal-to-Noise Ratio. The proposed methodology provides better CR and PSNR than the other state-of-the-art algorithm. Keywords Medical Image Sequences, Super-Spatial Structure Prediction, Lossless Compression, Motion Estimation and Motion Compensation, Inter-frame Coding, CALIC, LZ8 1. INTRODUCTION Hospitals and various medical organizations produce huge volume of digital medical image sequences which includes Computed Tomography (CT), Magnetic Resonance Image (MRI), Ultrasound and Capsule Endoscope (CE) images. These medical image sequences require considerable storage space [1].The solution to this problem could be the application of compression. Medical image compression is very important in the present world for efficient archiving and transmission of images. Image compression can be classified as lossy and lossless. In lossy compression scheme there is loss of information and the original image is not recovered exactly. Lossy scheme seems to be irreversible. But lossless scheme is reversible and this represents an image signed with the smallest possible number of bits without loss of any information thereby speeding up transmission and minimizing storage requirement. Lossless reproduces the original image without any quality loss [2].Medical imaging does not require lossy compression due to the following reason. The first reason is the incorrect diagnosis due to the loss of useful information. The second reason is the operations like image enhancement may emphasize the degradations caused by lossy compression. Hence efficient lossless compression methods are required for medical images [3].Lossless compression includes Discrete Cosine Transform, Wavelet Compression, Fractal Compression, Vector Quantization and Linear Predictive Coding. Lossless consist of two distinct and independent components called modeling and coding. The modeling generates a statistical model for the input data. The coding maps the input data to bit strings [4]. Several Lossless image compression algorithms were evaluated for compressing medical images. There are several lossless image compression algorithms like Lossless JPEG,JPEG 2000,PNG,CALIC and JPEG-LS.JPEG-LS has excellent coding and best possible compression efficiency[1].But the Super-Spatial Structure Prediction algorithm proposed in [5] has outperformed the JPEG-LS algorithm. This algorithm divides the image into two regions, structure regions (SRs) and non-structure regions (NSRs).The structure regions are encoded with Super-Spatial Structure Prediction technique and non-structure regions are encoded using JPEG-LS. The idea of Super-Spatial Structure Prediction is taken from video coding. There are many structures in a single image. These include edges, pattern and textures. This has relatively high computational efficiency. No codebook is required in this compression scheme because the structure components are searched within the encoded image regions [6]. JPEG-LS has excellent coding and best possible compression efficiency [1].This is a simple and baseline algorithm which consists of two independent and distinct stages called modeling and encoding. This is developed with the aim of providing a low complexity lossless and near- lossless image compression. This is based on LOCO-I (Low Complexity Lossless Compression for Images) algorithm [7] using adaptive prediction, context, modeling and Golomb coding. It supports near lossless compression by allowing a fixed maximum sample error. A continuous image is generally compressed best in JPEG-LS [8]. Most of the lossless image compression algorithms take only a single image independently without utilizing the correlation among the sequence of frames of MRI or CE images. Since there is too much correlation among the medical image sequences, we can achieve a higher compression ratio using inter-frame coding. The idea of compressing sequence of images was first adopted in [9] for lossless image compression and was used in [10], [11], [12] for lossless video compression. The Compression Ratio (CR) was significantly low (i.e.) 2.5 which was not satisfactory. Hence in [1] they have combined JPEG-LS with inter-frame coding to find the correlation among image sequences and the obtained ratio was 4.8.Super-Spatial Structure Prediction algorithm proposed in [13] has outperformed JPEG-LS. However this ratio can be enhanced using Super-Spatial Structure Prediction technique and LZ8.Super-Spatial Structure Prediction is applied with a simple block matching algorithm Binary Tree Search (BTS) [13]. LZ8 is used to further compress the output code of SSP [14]. In this paper, we propose a hybrid algorithm for medical image sequences. The proposed algorithm combines Super- Spatial Structure Prediction technique with MEMC and a new innovative scheme LZ8 to achieve a high compression ratio. The Compression Ratio (CR) can be calculated by the equation (1) and PSNR by equation (2)